Profiling proteomics

Acute profiling for several samples at once

Proteome profiling using labelling strategies has some advantages over label-free proteomics. For instance, differential chemical labelling allows sample multiplexing, i.e. pooling several experimental conditions in one MS run. This analysis method greatly reduces the variability that is associated with the reproducibility of the chromatographic separation, as the different conditions are analyzed all at once. Labelling experiments therefore provide the best possible comparison between treatments. However, these technique often require extensive sample fractionation before analysis, because multiplexing greatly increases sample complexity.

Compatible labelling workflows

• SILAC (stable isotope labelling with amino acids in cell culture)
• iTRAQ (isobaric amine-specific labelling, up to 8plex)
• mTRAQ (non-isobaric amine specific labelling, up to 3plex)
• Cysteine-targeted labelling
• And more (inquire for confirmation).

Data report

The data for profiling proteomics experiments will be delivered in a spreadsheet. We will provide basic statistics such as average, standard deviation and %CV and fold change for group comparisons. We will also provide an interactive data tab where you can visualize relevant information for a given protein. If needed, we can also generate more advanced statistical analyses, such as a Principal Component Analysis (PCA), heatmap clustering and gene ontology.